TeleEmbedBench is the first multi-corpus benchmark showing LLM-based embedding models significantly outperform traditional sentence-transformers on telecommunications specifications and code for retrieval accuracy and noise robustness.
Bert: Pre-training of deep bidirectional transformers for language understanding,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.
Speech from self-control tasks in remote learning shows perceptible emotional variations along valence, arousal, and dominance that can be automatically predicted.
citing papers explorer
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TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications
TeleEmbedBench is the first multi-corpus benchmark showing LLM-based embedding models significantly outperform traditional sentence-transformers on telecommunications specifications and code for retrieval accuracy and noise robustness.
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Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.
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Toward using Speech to Sense Student Emotion in Remote Learning Environments
Speech from self-control tasks in remote learning shows perceptible emotional variations along valence, arousal, and dominance that can be automatically predicted.